Abhishek Singh Pawar

My AI Learning Journey

From parsing logs to full‑stack observability: practical, production tools built with Python, Streamlit, and deep JVM knowledge—augmented by lightweight AI integrations.

Why AI matters here

MCP servers host lightweight training and inference pipelines close to telemetry, enabling continuous fine‑tuning on project data and low‑latency, context‑aware inference. Use them to run anomaly detectors, score and prioritize alerts, synthesize concise RCA summaries, and surface remediation suggestions — all of which reduce manual forensics and speed MTTR.

Automates incident triage Detects subtle anomalies Speeds root‑cause explanation

AI‑Powered Apache Logs Analyzer AI

Transforms raw Apache logs into structured insights, dashboards, and concise AI summaries.

Python 3.10+ Streamlit HuggingFace OpenAI

Highlights: Real‑time stream · AI Q&A (latest 50) · Exports · Neon UI

Problem it solves: Converts noisy raw Apache logs into actionable insights quickly, reducing time-to-diagnosis for web incidents.

Advantages:

Fast, real‑time visibility into traffic and errors
AI summaries accelerate incident triage
Exportable reports for audits and postmortems

View Code Download

AIPoweredApacheLogsAnalyzer/
├─ app.py
├─ parsers/
│  ├─ apache.py
│  └─ common.py
├─ ai/
│  ├─ summarize.py
│  └─ prompts/
├─ static/
├─ templates/
├─ requirements.txt
└─ README.md

Dependencies (requirements.txt snippet):
streamlit==1.24.0
pandas==2.1.0
transformers==4.31.0
openai==0.28.0

Tech: Python 3.10+, Streamlit, HuggingFace/OpenAI integrations, Docker-ready

Quickstart:
pip install -r requirements.txt
streamlit run app.py

LogsHeapAnalyzer AI

Analyze JVM heap dumps and GC logs to detect leaks, GC storms, and retention issues.

Python 3.10+ JVM / jmap Streamlit Matplotlib

Highlights: GC heatmaps · Object timelines · AI root‑cause · Correlation with logs

Problem it solves: Detects memory leaks and GC pathologies that are hard to reproduce, shortening mean-time-to-resolution for memory issues.

Advantages:

Visual GC heatmaps make anomalies obvious
Object timelines reveal retention sources
AI root‑cause reports reduce manual forensics

View Code Download

LogsHeapAnalyzer/
├─ analyzer.py
├─ parsers/
│  ├─ gc_parser.py
│  └─ heap_parser.py
├─ ui/
│  └─ streamlit_app.py
├─ samples/
├─ requirements.txt
└─ README.md

Dependencies (requirements.txt snippet):
numpy==1.26.0
pandas==2.1.0
streamlit==1.24.0
pyheapy==0.1.0

Tech: Python 3.10+, JVM heap/GC tooling (jmap/jcmd), Streamlit, Matplotlib/Plotly

Quickstart:
# capture heap using JDK tools (example):
# jmap -dump:live,format=b,file=heap.hprof 
pip install -r requirements.txt
streamlit run ui/streamlit_app.py

DeltaPerf‑AI AI

Unified performance intelligence combining logs, metrics, and heap analysis for multi‑angle AI insights.

Python 3.10+ Prometheus scikit-learn Streamlit

Highlights: Correlation engine · AI timeline narration · Deployment deltas · Dashboards

Problem it solves: Bridges siloed telemetry (logs, metrics, heap) so teams can quickly understand performance regressions across deployments.

Advantages:

Cross-source correlation surfaces root causes faster
AI‑narrated timelines speed stakeholder communication
Designed for production dashboards and regressions tracking

View Code Download

DeltaPerf-AI/
├─ services/
│  ├─ ingest.py
│  └─ correlator.py
├─ dashboards/
├─ model/
│  └─ drift_detector.py
├─ requirements.txt
└─ README.md

Dependencies (requirements.txt snippet):
pandas==2.1.0
scikit-learn==1.3.2
prometheus-client==0.16.0
streamlit==1.24.0
sqlalchemy==2.1.0

Tech: Python 3.10+, Prometheus metrics, Streamlit dashboards, Docker, scikit-learn (anomaly scoring)

Quickstart:
pip install -r requirements.txt
python services/ingest.py --config config.yml

Themes

Automated incident triage Cross‑source correlation AI root‑cause analysis Production dashboards JVM heap & GC tooling Streamlit quickstarts Open‑source integrations

Goal: reduce time‑to‑diagnosis, make observability actionable, and ship reproducible developer tools with clear quickstarts, dashboards, and integrations.